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--- |
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license: apache-2.0 |
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language: |
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- hu |
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metrics: |
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- accuracy |
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model-index: |
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- name: huBERTPlain |
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results: |
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- task: |
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type: text-classification |
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metrics: |
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- type: f1 |
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value: 0.77 |
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--- |
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## Model description |
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Cased fine-tuned BERT model for Hungarian, trained on (manuallay anniated) parliamentary pre-agenda speeches scraped from `parlament.hu`. |
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## Intended uses & limitations |
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The model can be used as any other (cased) BERT model. It has been tested recognizing emotions at the sentence level in (parliamentary) pre-agenda speeches, where: |
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* 'Label_0': Neutral |
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* 'Label_1': Fear |
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* 'Label_2': Sadness |
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* 'Label_3': Anger |
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* 'Label_4': Disgust |
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* 'Label_5': Success |
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* 'Label_6': Joy |
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* 'Label_7': Trust |
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## Training |
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Fine-tuned version of the original huBERT model (`SZTAKI-HLT/hubert-base-cc`), trained on HunEmPoli corpus. |
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| Category | Count | Ratio | Sentiment | Count | Ratio | |
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| -------- | ----- | ------ | --------- | ----- | ------ | |
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| Neutral | 351 | 1.85% | Neutral | 351 | 1.85% | |
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| Fear | 162 | 0.85% | Negative | 11180 | 58.84% | |
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| Sadness | 4258 | 22.41% | |
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| Anger | 643 | 3.38% | |
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| Disgust | 6117 | 32.19% | |
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| Success | 6602 | 34.74% | Positive | 7471 | 39.32% | |
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| Joy | 441 | 2.32% | |
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| Trust | 428 | 2.25% | |
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| Sum | 19002 | | | | | |
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## Eval results |
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| Class | Precision | Recall | F-Score | |
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|-----|------------|------------|------| |
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| Fear | 0.625 | 0.625 | 0.625 | |
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| Sadness | 0.8535 | 0.6291 | 0.7243 | |
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| Anger | 0.7857 | 0.3437 | 0.4782 | |
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| Disgust | 0.7154 | 0.8790 | 0.7888 | |
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| Success | 0.8579 | 0.8683 | 0.8631 | |
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| Joy | 0.549 | 0.6363 | 0.5894 | |
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| Trust | 0.4705 | 0.5581 | 0.5106 | |
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| Macro AVG | 0.7134 | 0.6281 | 0.6497 | |
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| Weighted AVG | 0.791 | 0.7791 | 0.7743 | |
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## Usage |
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```py |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("poltextlab/HunEmBERT8") |
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model = AutoModelForSequenceClassification.from_pretrained("poltextlab/HunEmBERT8") |
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``` |
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### BibTeX entry and citation info |
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If you use the model, please cite the following paper: |
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Bibtex: |
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```bibtex |
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@ARTICLE{10149341, |
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author={{"U}veges, Istv{\'a}n and Ring, Orsolya}, |
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journal={IEEE Access}, |
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title={HunEmBERT: a fine-tuned BERT-model for classifying sentiment and emotion in political communication}, |
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year={2023}, |
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volume={11}, |
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number={}, |
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pages={60267-60278}, |
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doi={10.1109/ACCESS.2023.3285536} |
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} |
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``` |
